Classifications

• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
  • A61B5/0075—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence by spectroscopy, i.e. measuring spectra, e.g. Raman spectroscopy, infrared absorption spectroscopy
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B1/00—Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopes; Illuminating arrangements therefor
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
  • A61B5/0071—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence by measuring fluorescence emission
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
  • A61B5/0082—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence adapted for particular medical purposes
  • A61B5/0084—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence adapted for particular medical purposes for introduction into the body, e.g. by catheters
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
  • A61B5/0082—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence adapted for particular medical purposes
  • A61B5/0091—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence adapted for particular medical purposes for mammography
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/48—Other medical applications
  • A61B5/4887—Locating particular structures in or on the body
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
  • A61B5/6846—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be brought in contact with an internal body part, i.e. invasive
  • A61B5/6847—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be brought in contact with an internal body part, i.e. invasive mounted on an invasive device
  • A61B5/6848—Needles
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
  • A61B5/6846—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be brought in contact with an internal body part, i.e. invasive
  • A61B5/6847—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be brought in contact with an internal body part, i.e. invasive mounted on an invasive device
  • A61B5/6852—Catheters
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
  • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
  • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
  • G01N21/64—Fluorescence; Phosphorescence
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
  • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
  • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
  • G01N21/64—Fluorescence; Phosphorescence
  • G01N2021/6417—Spectrofluorimetric devices
  • G01N2021/6421—Measuring at two or more wavelengths

Definitions

• the invention relates to a method of and to a system for tissue discrimination on the basis of spectroscopic measurements and analysis.
• a problem with classifying tissue type of an individual patient using such a database is that inter-patient variance hampers tissue discrimination. It has been demonstrated that in breast tissue the fat content increases whereas glandular tissue decreases with age. Therefore, a very large standard deviation in fat and collagen exists due to a wide range of age.
• the sensitivity and specificity of the methods using spectroscopic point measurements are moderate (50-85%) and results strongly vary in the literature. The moderate sensitivity makes the approach not optimal for an individual patient approach.
• Fig. 2 shows inter-patient variation in a score plot of partial least squares discriminant analysis (PLS-DA) prediction scores of breast tissue classification of all patients. More specifically, Fig. 2 shows a result of an all-patient PLS-DA analysis of diffuse reflectance spectroscopic measurements on ex-vivo human breast tissues samples. From Fig. 2 it can be seen that inter-patient variation is an issue when discriminating for example fibroadenoma (FA, benign) from gland (G, normal) tissue, the gland tissue represented by symbols " " which are scattered and mix up with other tissue type measurements.
• F fibroadenoma
• F fibroadenoma
• A adenocarcinoma
• DCIS ductal carcinoma in situ
• F fat
• a discrimination would be desirable, which is tuned to the individual patient, where spectroscopic measurements at different positions such as in normal and/or benign and malignant tissues in the individual patient are obtained, if possible, a
• classification model can be provided using individual patient data and a priori spectroscopic and clinical patient knowledge, and tissue types are classified for the patient without hindrance of inter-patient variance.
• optical spectra are acquired from a position inside the body and processed so as to discriminate tissue based on a procedure where data of a first tissue type class is collected (e.g. normal tissue) using at least one classification threshold which is defined relative to this first tissue type class. It is thus assured on the basis of the image guidance that the measurement relates to normal tissue.
• the center of gravity of the reference measurements is based on the actual individual patient, while in conventional systems this is based on the many-patient database.
• the basis of normal spectroscopic measurements can thus be tuned to the individual patient characteristic. Discriminating the normal plus benign and malignant from that reference is more efficient compared to the reference of the all patient database.
• the procedure to obtain the threshold for different tissue type classes includes obtaining spectroscopic measurements by means of a device like a needle, preferably at several different positions, e.g. in normal tissue of the patient using image guidance and/or the experience of the physician as a starting point.
• the spectral absorption and scattering characteristics or fluorescence characteristics derived from fits (e.g. on hemoglobin, fat, beta carotene, bilirubin, water content and amount of scattering) of these reference measurements define these data points to form a first tissue type class.
• the a priori knowledge on the spectral characteristics of a reference tissue is used to define a threshold for the cloud of data points that belong to this reference set.
• the needle or other interventional device e.g. catheter or endoscope or the like
• the needle or other interventional device e.g. catheter or endoscope or the like
• a second tissue type class is defined. These measured tissue positions could be suspicious, and in a practical situation the physician could based on the spectral information decide to take a biopsy.
• the predetermined spectral characteristics may comprise absorption and scattering characteristics, or fluorescence characteristics.
• the console may be arranged for recognizing data points of the second tissue type class from the shape of a plot of data points.
• the discrimination may be based on a clustering approach which allows easy and simple detection.
• the first tissue type class may relate to fat or gland tissue and the second tissue type class relates to adenocarcinoma or ductal carcinoma in situ tissue.
• any type of tissue can be discriminated, such as different normal types of tissue, or normal and diseased tissue, or normal and tumor tissue, or normal tissue, benign and malignant tissue.
• the console may be arranged for defining a third tissue type class and using the orientation of a triangle defined by data points of the three tissue type classes to assign a tissue type to the second and third tissue type classes.
• spectral characteristics of measurements in different positions in the tissue may make up additional classes and thresholds between the classes.
• the console may be arranged for using a correlation of measured spectra with a database of individual patient spectra to discriminate among the different tissue types.
• a correlation of measured spectra with a database of individual patient spectra to discriminate among the different tissue types.
• the console may be arranged for generating a reference map in a space defined by at least two of extracted water, lipid and collagen fractions, for classifying the space based in the tissue types, and for tagging the spectroscopic measurement on the reference map.
• a reference map in a space defined by at least two of extracted water, lipid and collagen fractions, for classifying the space based in the tissue types, and for tagging the spectroscopic measurement on the reference map.
• Fig. 1 shows a schematic block diagram of a medical apparatus according to various embodiments
• Fig. 2 shows a score plot of PLS-DA predictions with inter-patient variations
• Figs. 3a and 3b show score plots of PLS-DA predictions with intra-patient variations and threshold lines according to a first embodiment
• Fig. 4 shows a score plot of PLS-DA predictions with intra-patient variations and threshold lines according to a second embodiment
• Fig. 5 shows a flow diagram of a correlation-based classification accorrding to a third embodiment
• Fig. 6 shows a score plot of the correlation-based classification with intra- patient variations and a threshold line as obtained for an ex- vivo spectra after biopsy according to the third embodiment according to the third embodiment;
• Fig. 7 shows a score plot of PLS-DA predictions with intra-patient variations and threshold lines
• Fig. 8 shows a score plot of PLS-DA classification with intra-patient variations for another ex- vivo spectra after biopsy according to the third embodiment
• Fig. 9 shows a reference map with estimated volume fractions for classification and with measurement tracks according to a fourth embodiment.
• a system as used in the following embodiments performs spectroscopic measurements of tissue under investigation,
• the system is configured to analyze these measurements in order to determine the type of the tissue.
• the system may use one or more analyzing methods that are generally known in the art. Generally known methods are described for example in Chemometric Methods in Process Analysis, Karl S. Booksh in; Encyclopedia of Analytical Chemistry; R.A. Meyers (Ed.); pp. 8145-8169; John Wiley & Sons Ltd, Chichester, 2000, or Partial Least-Squares Methods for Spectral Analyses. 1.; Relation to Other Quantitative Calibration Methods and the Extraction of Qualitative Information; David M. Haaland* and Edward V.
• PLS-DA is described in Classification of Adipose Tissue Species using Raman Spectroscopy; J. Renwick Beattie E Steven E. J. Bell JE; Claus Borggaard JE Anna M. Fearon JE; Bruce W. Moss; Lipids (2007) 42:679-685.
• the system according to the following embodiments is an improvement of the medical device consisting of a console and an optical probe as is for instance described in R. Nachabe et al., "Estimation of biological chromophores using diffuse optical spectroscopy: benefit of extending the UV-VIS wavelength range to include 1000 to 1600 nm", Biomedical Optics Express 1 , 2010.
• Various embodiments of the method and of the system according to the invention are described in more detail below.
• Fig. 1 shows a medical apparatus 100 according to various embodiments of the present invention.
• the medical apparatus 100 comprises an optical instrument 1 10 and a medical device.
• the medical device is a photonic needle 130.
• it may be any other medical device or probe which allows spectroscopic tissue measurements, e.g., any optical probe or catheter-type device.
• the medical apparatus 100 is in particular suitable for optically investigating tissue material which may be surrounded laterally with respect to the optical probe 130.
• the optical instrument or console 110 comprises a light source 111, which is adapted to generate illumination light 112.
• the light source 111 may be a laser which emits a monochromatic radiation beam.
• the illumination light is directed via a first optic 113 onto a first fiber end 141 of an optical fiber 140.
• the console 110 further comprises a spectrometer device 116 which is optically coupled to an optical fiber 145 by means of a second optic 118.
• the spectrometer device 116 is used for spectrally analyzing measurement light 117, which is provided by the photonic needle 130.
• the spectrometer device 116 may be provided with a charged coupled device (CCD) camera 119 in order to detect the measurement light 117, which is spectrally expanded by means of at least one refractive or diffractive optical element of the CCD camera 119 .
• CCD charged coupled device
• the photonic needle 130 may comprises an elongated body having a longitudinal axis. On a side wall of the elongated body there may be provided second fiber ends, which are coupled to the optical fiber 140. The second fiber ends may be oriented in such a manner, that they provide each a lateral field of view which might be used for illuminating tissue laterally surrounding the elongated body.
• the photonic needle 130 may further comprises a waveguide end, which is arranged at a front end of the elongated body to provide a front field of view which is oriented substantially parallel to the longitudinal axis.
• the two optical fibers 140 and 145 may be optically coupled to the second fiber ends and to the front waveguide end in various combinations. Thereby, the ends may be coupled collectively or individually with the optical fiber 140 respectively the optical fiber 145.
• the outlets, which are optically coupled to the optical fiber 145 respectively the spectrometer device 116 represent de facto an optical inlet, because measurement light, which has been scattered by the tissue, can enter these inlet such that this measurement light can be analyzed by means of the spectrometer device 116.
• the lateral fiber ends of the photonic needle 130 are assigned to the same optical fiber 140. However, it may also be possible to use one separate optical fiber for each lateral fiber end and/or for the front waveguide end. Of course, also less or more than two lateral fiber ends might be provided at the side wall of the elongated body of the photonic needle 130.
• the photonic needle 130 is positioned using image guidance in normal tissue, e.g. fat in breast tissue.
• normal tissue e.g. fat in breast tissue.
• the characteristic spectral features of lipids at 1210 nm can be used as a guide for the eye, or by using in the console 110 or the spectrometer device 116 a real-time fat fitting model that determines amongst other parameters the amount of fat, or a trained classification model (such as principal component analysis (PCA), or partial least squares (PLS)) on fat spectroscopic measurements.
• PCA principal component analysis
• PLS partial least squares
• a deteremined threshold in a PLS-DA model e.g. PLS-DA score 3>0.5
• the tissue could be suspicious. If the goal of the clinician is to target suspicious tissue he/she could decide here to take for instance a biopsy.
• Figs. 3a and 3b show examples of classification of spectroscopic data on (ex- vivo) breast tissue of individual patients. More specifically, intra-patient variation is shown in the score plots of PLS-DA predictions of breast tissue classification of two different individual patients.
• the symbols of Fig. 2 are also used to distinguish among different tissue type measurements in the diagrams.
• Fig. 3a fat (F), gland (G) and adenocarcinoma (A) tissues were measured
• Fig. 3b fat (F), gland (G) and ductal carcinoma in situ (DCIS) tissues were measured.
• the axes in Figs. 3a and 3b represent PLS-DA score 2 versus PLS-DA score 3.
• the threshold is defined relative to the first tissue class, hence fat, in this case..
• the example relates to ex- vivo tissue, the invention can equally well be applied to in-vivo tissue.
• Figs. 3a and 3b show intra- patient variation (and separation) of three different tissue types.
• the malignant types of tissue can now be determined with respect to this first class.
• the situation shown in Figs. 3a and 3b can occur, where three different clouds of data points appear on the basis of classification (such as PLS- DA) prediction scores.
• classification such as PLS- DA
• typical triangular shapes of data clouds can be used to differentiate normal versus malignant tissue classes.
• malignant data points can be recognized from the shape of this plot. For example different malignant tissue types show up at different positions in the score plots.
• fat and gland tissue i.e.
• the discrimination procedure of the console 110 or the spectrometer device 116 starts with a normal class. Then, it proceeds to determine a second tissue class relative to the first class, and proceeds to define a third class relative to the first and second classes. The orientation of the "triangle" defined by the three classes is then used by the console 110 or the spectrometer device 116 to finally assign to class 2 and 3 the type of malignancy, i.e. DCIS or adenocarcinoma.
• Fig. 4 shows a score plot of PLS-DA predictions of breast tissue classification of an individual patient with four different tissue types (e.g. fat (F), gland (G), DICS and adenocarcinoma (A)). It is noted that the malignant tissue type classes of DCIS and adenocarcinoma are separated here in the PLS-DA score 3 versus PLS-DA score 4 plot.
• tissue types e.g. fat (F), gland (G), DICS and adenocarcinoma (A)
• the above thresholds are defined relative to the first class of tissue which is fat in this case.
• Fig. 4 the four different tissue type classes are presented in score plot axes 3 versus 4. Now, the normal tissue type classes of fat and gland are closely together and both malignant types DCIS and adenocarcinoma are separated in the two-dimensional plane of PLS-DA scores 3 and 4.
• Fig. 5 shows a schematic flow diagram of a discrimination and classification procedure for separating normal (and/or benign) tissue from malignant tissue according to a third embodiment.
• the flow diagram of Fig. 5 is based on a correlation classification model where each spectrum is stored in a patient database.
• a correlation of measured spectra with the database of individual patient spectra is used.
• Such a database can be build from adding subsequent measured spectra in classes based on the spectral characteristics.
• step SI 10 a spectrum spec(i) of probed tissue is measured and read.
• the running parameter I is larger than one and smaller than a maximum value (i.e. i>l and i ⁇ last)
• the measured spectrum spec(i) is correlated in step S120 with the spectra stored in the patient database (DB).
• a predetermined threshold here: 0.5
• the measured spectrum spec(i) is classified in step SI 30.
• the classification result is determined.
• the procedure ends at step S170.
• Fig. 6 shows a plot of correlation coefficients vs. spectrum number obtained from an analysis a plurality of measured spectra of breast tissue of a single patient.
• a correlation coefficient threshold of 0.85 (dashed horizontal line in Fig. 6) can be defined to separate normal tissue (fat and gland tissue) from malignant tissue
• a first set of measured spectra No. 1 to 8 has been obtained from fat tissue
• a second set of measured spectra No. 9 to 13 has been obtained from glandular tissue
• a third set of measured spectra No. 14 to 30 has been obtained from adenocarcinoma tissue.
• a single threshold can be used to separate normal tissue (fat and gland tissues) from malignant (adenocarcinoma) tissue.
• Fig. 7 shows a score plot of PLS-DA predictions with intra-patient variations and threshold lines as obtained for an ex-vivo spectra after biopsy according to the third embodiment.
• the breast tissue spectra are classified into classes normal and benign tissue (squares) versus malignant tissue (circles) for another individual patient.
• a single threshold can be defined e.g. PLS-DA score 1 ⁇ 0.5 that classifies the spectra in Malignant type.
• Threshold PLS-DA score 2 > 0.5 does the same.
• the example shown here shows that a simple correlation threshold can give an indication on the type of tissue at the tip of the probe and could help to decide if a biopsy at this location is needed or not.
• Fig. 8 shows a score plot of a PLS-DA classification with intra-patient variations for another ex-vivo spectra after biopsy according to the third embodiment.
• liver tissue has been examined. It can be seen that the same threshold setting also works for this liver patient in tissue type classes normal and benign (squares) vs. malignant (circles).
• a single threshold PLSDA score 1 ⁇ 0.5 classifies the spectra in malignant tissue class type.
• the threshold PLSDA score 2 > 0.5 does the same.
• Fig. 9 shows a reference map with estimated volume fractions for classification and with measurement tracks according to a fourth embodiment.
• clinical parameters can be used, that can be derived from a model that fits the measurements and extract water (W), lipid (L) and collagen (C) fractions.
• Fig. 9 shows the fat vs water and fat vs collagen estimated volume fractions and the two-dimensional (2D) classification of these spaces.
• These 2D maps can be used by the console 110 or the spectrometer device 116 as reference maps and each measurement within a patient can be tagged on the map and tracked interactively for each new measurement acquired within a single patient as depicted in the measurement tracks of Fig. 9.
• the classification can be reduced from 5 to 3 classes in a fifth embodiment.
• Ex- vivo diffuse reflectance spectroscopy was performed on normal and malignant breast tissue from 24 female breast cancer patients. Tissue samples from macroscopic normal adipose tissue, glandular tissue, DCIS and invasive carcinoma were included in the optical analysis. Optical spectra were collected over a wavelength range from 500 to 1600 nm. Model based data analysis was performed on the collected tissue spectra from all patients collectively and each patient individually. Results were compared to histology analysis.
• a system and method for discrimination of malignant tissue from normal and benign tissue in a single patient on the basis of optical spectroscopic measurements has been described.
• reference values are obtained for the normal class.
• data points can be assigned to new class(es) when the spectral characteristics fall outside a threshold defining the reference class. Thresholds between different classes can also be defined.
• Finding (the transition to) malignant tissue can be based on comparing the spectroscopic values to the classification threshold discriminating normal and benign versus malignant tissue.

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